{"id":"W4376504325","doi":"10.1007/s10877-023-01028-y","title":"Deep learning classification of capnography waveforms: secondary analysis of the PRODIGY study","year":2023,"lang":"en","type":"article","venue":"Journal of Clinical Monitoring and Computing","topic":"Cardiovascular Syncope and Autonomic Disorders","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; University of Toronto; University Health Network","funders":"","keywords":"Capnography; Waveform; Medicine; Computer science; Pattern recognition (psychology); Artificial intelligence; Anesthesia","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008435136,0.0004993684,0.0006307981,0.00070899,0.0002174119,0.0007835467,0.0003685505,0.0005315687,0.001698438],"category_scores_gemma":[0.002550222,0.0001154003,0.0004163623,0.0003861216,0.0001562987,0.0002928377,0.0004944567,0.0007290679,0.0005465723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001716779,"about_ca_system_score_gemma":0.0003010629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002358585,"about_ca_topic_score_gemma":0.00265244,"domain_scores_codex":[0.9997668,0.00007924651,0.00001604161,0.00005823984,0.00003487966,0.00004478069],"domain_scores_gemma":[0.9990938,0.0003502016,0.00008155616,0.0001504186,0.00021033,0.0001137441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009472928,0.001897584,0.6320173,0.0003101734,0.0009914269,0.001695759,0.0003541437,0.007132486,0.04116403,0.0009729322,0.01481504,0.2891762],"study_design_scores_gemma":[0.0003278592,0.001639435,0.8888147,0.00008217228,0.0004713762,0.001800545,0.0003763556,0.08977805,0.00970827,0.001260902,0.005687171,0.000053205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917316,0.0004939574,0.004461855,0.0002433883,0.00007089539,0.00004862152,0.00179486,0.00009853527,0.001056224],"genre_scores_gemma":[0.9926835,0.0001823261,0.00224933,0.00006620155,0.00009674396,0.00003890853,0.003418434,0.00004383173,0.001220798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002358585,"threshold_uncertainty_score":0.005681813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04927737474842428,"score_gpt":0.3714041639876982,"score_spread":0.3221267892392738,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}